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Quality of Life for Transition-Age Youth with Autism or Intellectual Disability
We examined the subjective health and well-being of 389 transition-age youth with autism or intellectual disability using the parent proxy version of the KIDSCREEN-27. Parents reported well-being of youth with autism and youth with intellectual disability lower than a normative sample in the domains of Physical Well-being, Psychological Well-being, and Social Support and Peers. For both groups of young people, the lowest ratings were reported in Social Support and Peers. Higher ratings of well-being in one or more domains were predicted by minority status, youth character strengths, involvement in community activities, and religious faith. Challenging behaviors, autism, age, and speech as the primary mode of communication were predictive of lower ratings of well-being. We discuss implications for special educators and service providers and offer directions for future research.
Journal Article
Promoting Social Interactions and Job Independence for College Students with Autism or Intellectual Disability: A Pilot Study
The employment outcomes for young adults with autism or intellectual disability (ID) lag far behind those of their peers without disabilities. Most postsecondary education programs for students with disabilities incorporate internship experiences to foster employment skills. However, the proximity of job coaches may inadvertently hinder social opportunities and independence. We used a multiple-probe, single-case experimental design across three college students with autism or ID to examine the effects of a coaching package on task engagement and social interactions. For all participants, interactions increased and task engagement maintained when job coaches reduced proximity and delivered prompts discreetly through bug-in-ear devices. Participants considered the intervention beneficial and unobtrusive. We present implications for supporting employment preparation within postsecondary education programs.
Journal Article
Prevalence of dementia in intellectual disability using different diagnostic criteria
Diagnosis of dementia is complex in adults with intellectual disability owing to their pre-existing deficits and different presentation.
To describe the clinical features and prevalence of dementia and its subtypes, and to compare the concurrent validity of dementia criteria in older adults with intellectual disability.
The Becoming Older with Learning Disability (BOLD) memory study is a two-stage epidemiological survey of adults with intellectual disability without Down syndrome aged 60 years and older, with comprehensive assessment of people who screen positive. Dementia was diagnosed according to ICD-10, DSM-IV and DC-LD criteria.
The DSM-IV dementia criteria were more inclusive. Diagnosis using ICD-10 excluded people with even moderate dementia. Clinical subtypes of dementia can be recognised in adults with intellectual disability. Alzheimer's dementia was the most common, with a prevalence of 8.6% (95% CI 5.2-13.0), almost three times greater than expected.
Dementia is common in older adults with intellectual disability, but prevalence differs according to the diagnostic criteria used. This has implications for clinical practice.
Journal Article
Pinned
Adonis is smart, intellectually gifted and born without legs; Autumn is strong, a great wrestler, and barely able to read in ninth grade--but Autumn is attracted to Adonis and determined to make him a part of her life whatever he or her best friend thinks.
Book
‘Step into her World’: Including the Voices of Learning-Disabled Children and Young People in Research
The voices of learning-disabled children and young people are rarely heard in qualitative research, particularly when they have complex communication needs. The current study was motivated by a desire to identify and co-produce effective and ethical qualitative research methods that would be capable of supporting the inclusion of this group of children and young people. We interviewed 12 parents of children with chromosomal learning disabilities and 6 professionals working with learning-disabled children, and we also observed 9 learning-disabled children and young people in classroom settings in order to develop methodological recommendations. Transcripts and field notes from these (n = 27) interviews and observations were analysed using Template Analysis, supplemented with reflexive Thematic Analysis. We identified ideas regarding the most effective communication and data collection strategies for this group; thoughts on the challenges to including learning-disabled voices in research; ethical considerations and the characteristics and experience required to be an effective researcher in this area. There was a good consensus that it would be highly valuable to develop more innovative and inclusive qualitative research methods for this group of people, and that it is vital that a careful, ethical and co-produced approach is taken to this endeavour. The next step will be to use these proposals to explore the issue with learning-disabled children and young people themselves.
Journal Article
Applications of Machine Learning in Real-Life Digital Health Interventions: Review of the Literature
Machine learning has attracted considerable research interest toward developing smart digital health interventions. These interventions have the potential to revolutionize health care and lead to substantial outcomes for patients and medical professionals.
Our objective was to review the literature on applications of machine learning in real-life digital health interventions, aiming to improve the understanding of researchers, clinicians, engineers, and policy makers in developing robust and impactful data-driven interventions in the health care domain.
We searched the PubMed and Scopus bibliographic databases with terms related to machine learning, to identify real-life studies of digital health interventions incorporating machine learning algorithms. We grouped those interventions according to their target (ie, target condition), study design, number of enrolled participants, follow-up duration, primary outcome and whether this had been statistically significant, machine learning algorithms used in the intervention, and outcome of the algorithms (eg, prediction).
Our literature search identified 8 interventions incorporating machine learning in a real-life research setting, of which 3 (37%) were evaluated in a randomized controlled trial and 5 (63%) in a pilot or experimental single-group study. The interventions targeted depression prediction and management, speech recognition for people with speech disabilities, self-efficacy for weight loss, detection of changes in biopsychosocial condition of patients with multiple morbidity, stress management, treatment of phantom limb pain, smoking cessation, and personalized nutrition based on glycemic response. The average number of enrolled participants in the studies was 71 (range 8-214), and the average follow-up study duration was 69 days (range 3-180). Of the 8 interventions, 6 (75%) showed statistical significance (at the P=.05 level) in health outcomes.
This review found that digital health interventions incorporating machine learning algorithms in real-life studies can be useful and effective. Given the low number of studies identified in this review and that they did not follow a rigorous machine learning evaluation methodology, we urge the research community to conduct further studies in intervention settings following evaluation principles and demonstrating the potential of machine learning in clinical practice.
Journal Article
Antiseizure medications prescribing for behavioural and psychiatric concerns in adults with an intellectual disability living in England
Antiseizure medications (ASMs) are the second most widely prescribed psychotropic for people with intellectual disabilities in England. Multiple psychotropic prescribing is prevalent in almost half of people with intellectual disabilities on ASMs. This analysis identifies limited evidence of ASM benefit in challenging behaviour management and suggests improvements needed to inform clinical practice.
Journal Article